Measuring Health Care Work–Related Contextual Factors: Development of the McGill Context Tool
Bibliographic record
Abstract
INTRODUCTION: Contextual factors can influence healthcare professionals' (HCPs) competencies, yet there is a scarcity of research on how to optimally measure these factors. The aim of this study was to develop and validate a comprehensive tool for HCPs to document the contextual factors likely to influence the maintenance, development, and deployment of professional competencies. METHODS: We used DeVellis' 8-step process for scale development and Messick's unified theory of validity to inform the development and validation of the context tool. Building on results from a scoping review, we generated an item pool of contextual factors articulated around five themes: Leadership and Agency, Values, Policies, Supports, and Demands. A first version of the tool was pilot tested with 127 HCPs and analyzed using the classical test theory. A second version was tested on a larger sample (n = 581) and analyzed using the Rasch rating scale model. RESULTS: First version of the tool: we piloted 117 items that were grouped as per the themes related to contextual factors and rated on a 5-point Likert scale. Cronbach alpha for the set of 12 retained items per scale ranged from 0.75 to 0.94. Second version of the tool included 60 items: Rasch analysis showed that four of the five scales (ie, Leadership and Agency, Values, Policies, Supports) can be used as unidimensional scales, whereas the fifth scale (Demands) had to be split into two unidimensional scales (Demands and Overdemands). DISCUSSION: Validity evidence documented for content and internal structure is encouraging and supports the use of the McGill context tool. Future research will provide additional validity evidence and cross-cultural translation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".